Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. The input dataset format is guessed from the --source argument

Convert legacy network pickle into the native PyTorch format. The tool is able to load the main network configurations exported using the TensorF

Generate images using pretrained network pickle. Examples: \b # Generate curated MetFaces images without truncation (Fig.10 left) py

Generate images using pretrained network pickle. Examples: \b python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,82

Generate images using pretrained network pickle. Examples: \b python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,82

Finds the python object with the given name.

Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.

Determine whether the given object is a valid URL string.

Train a GAN using the techniques described in the paper "Training Generative Adversarial Networks with Limited Data". Examples: \b #

r"""Returns the number of scalars that were accumulated for the given statistic between the last two calls to `update()`, or zero if n

r"""Class decorator that extends a given class to save its source code when pickled. Example: from torch_utils import persistence

r"""Broadcasts the given set of scalars to all interested instances of `Collector`, across device and process boundaries. This function is ex

Project given image to the latent space of pretrained network pickle. Examples: \b python projector.py --outdir=Interpolation/person --ta

r"""Convenience getter. `collector[name]` is a synonym for `collector.mean(name)`.